Journal
INSTRUMENTATION SCIENCE & TECHNOLOGY
Volume 37, Issue 3, Pages 327-344Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/10739140902831990
Keywords
Atherosclerotic; Clusters; Intelligent instrumentation; Pattern recognition; Raman spectroscopy; Signal processing
Funding
- CNP2 (National Counsel of Technological and Scientific Development) [PQ2-305610/2008-2]
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The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion's biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.
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